tf.layers.Layer

Class Layer

Arguments:

trainable: Boolean, whether the layer's variables should be trainable.

name: String name of the layer.

dtype: Default dtype of the layer's weights (default of None means use the
type of the first input).

Read-only properties:
* name: The name of the layer (string).
* dtype: Default dtype of the layer's weights (default of None means use the
type of the first input).
* trainable_variables: List of trainable variables.
* non_trainable_variables: List of non-trainable variables.
* variables: List of all variables of this layer, trainable and
non-trainable.
* updates: List of update ops of this layer.
* losses: List of losses added by this layer.
* trainable_weights: List of variables to be included in backprop.
* non_trainable_weights: List of variables that should not be
included in backprop.
* weights: The concatenation of the lists trainable_weights and
non_trainable_weights (in this order).

Mutable properties:
* trainable: Whether the layer should be trained (boolean).
* input_spec: Optional (list of) InputSpec object(s) specifying the
constraints on inputs that can be accepted by the layer.

__init__

__init__(
trainable=True,
name=None,
dtype=None,
**kwargs
)

Properties

activity_regularizer

Optional regularizer function for the output of this layer.

dtype

graph

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input,
i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

Raises:

RuntimeError: If called in Eager mode.

AttributeError: If no inbound nodes are found.

input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node,
i.e. if it is connected to one incoming layer.

Returns:

Input mask tensor (potentially None) or list of input
mask tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input,
i.e. if it is connected to one incoming layer, or if all inputs
have the same shape.

Methods

__call__

Arguments:

**kwargs: additional keyword arguments to be passed to self.call.
Note: kwarg scope is reserved for use by the layer.

Returns:

Output tensor(s).

Note:
- If the layer's call method takes a scope keyword argument,
this argument will be automatically set to the current variable scope.
- If the layer's call method takes a mask argument (as some Keras
layers do), its default value will be set to the mask generated
for inputs by the previous layer (if input did come from
a layer that generated a corresponding mask, i.e. if it came from
a Keras layer with masking support.

compute_mask

Arguments:

Returns:

None or a tensor (or list of tensors,
one per output tensor of the layer).

compute_output_shape

compute_output_shape(input_shape)

Computes the output shape of the layer.

Assumes that the layer will be built
to match that input shape provided.

Arguments:

input_shape: Shape tuple (tuple of integers)
or list of shape tuples (one per output tensor of the layer).
Shape tuples can include None for free dimensions,
instead of an integer.

Returns:

An input shape tuple.

count_params

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

ValueError: if the layer isn't yet built
(in which case its weights aren't yet defined).

from_config

from_config(
cls,
config
)

Creates a layer from its config.

This method is the reverse of get_config,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights).

Arguments:

config: A Python dictionary, typically the
output of get_config.

Returns:

A layer instance.

get_config

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.

The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network (one layer of abstraction above).

Returns:

Python dictionary.

get_input_at

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0 will correspond to the
first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

Raises:

RuntimeError: If called in Eager mode.

get_input_mask_at

get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0 will correspond to the
first time the layer was called.

Returns:

A mask tensor
(or list of tensors if the layer has multiple inputs).

get_input_shape_at

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0 will correspond to the
first time the layer was called.

Returns:

A shape tuple
(or list of shape tuples if the layer has multiple inputs).

Raises:

RuntimeError: If called in Eager mode.

get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

inputs: Input tensor or list/tuple of input tensors.

Returns:

List of loss tensors of the layer that depend on inputs.

Raises:

RuntimeError: If called in Eager mode.

get_output_at

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0 will correspond to the
first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

Raises:

RuntimeError: If called in Eager mode.

get_output_mask_at

get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0 will correspond to the
first time the layer was called.

Returns:

A mask tensor
(or list of tensors if the layer has multiple outputs).

get_output_shape_at

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0 will correspond to the
first time the layer was called.

Returns:

A shape tuple
(or list of shape tuples if the layer has multiple outputs).

Raises:

RuntimeError: If called in Eager mode.

get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

inputs: Input tensor or list/tuple of input tensors.

Returns:

List of update ops of the layer that depend on inputs.

Raises:

RuntimeError: If called in Eager mode.

get_weights

get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

set_weights

set_weights(weights)

Sets the weights of the layer, from Numpy arrays.

Arguments:

weights: a list of Numpy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the layer (i.e. it should match the
output of get_weights).

Raises:

ValueError: If the provided weights list does not match the
layer's specifications.